Overview

Dataset statistics

Number of variables18
Number of observations35064
Missing cells7015
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Alerts

station has constant value ""Constant
CO is highly overall correlated with NO2 and 3 other fieldsHigh correlation
DEWP is highly overall correlated with PRES and 1 other fieldsHigh correlation
NO2 is highly overall correlated with CO and 3 other fieldsHigh correlation
No is highly overall correlated with yearHigh correlation
O3 is highly overall correlated with TEMPHigh correlation
PM10 is highly overall correlated with CO and 2 other fieldsHigh correlation
PM2.5 is highly overall correlated with CO and 2 other fieldsHigh correlation
PRES is highly overall correlated with DEWP and 1 other fieldsHigh correlation
SO2 is highly overall correlated with CO and 1 other fieldsHigh correlation
TEMP is highly overall correlated with DEWP and 2 other fieldsHigh correlation
year is highly overall correlated with NoHigh correlation
PM2.5 has 779 (2.2%) missing valuesMissing
PM10 has 656 (1.9%) missing valuesMissing
SO2 has 730 (2.1%) missing valuesMissing
NO2 has 1234 (3.5%) missing valuesMissing
CO has 2012 (5.7%) missing valuesMissing
O3 has 1214 (3.5%) missing valuesMissing
RAIN is highly skewed (γ1 = 29.5690635)Skewed
No is uniformly distributedUniform
No has unique valuesUnique
hour has 1461 (4.2%) zerosZeros
RAIN has 33663 (96.0%) zerosZeros
WSPM has 417 (1.2%) zerosZeros

Reproduction

Analysis started2024-03-08 05:08:29.828581
Analysis finished2024-03-08 05:09:11.380923
Duration41.55 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

No
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct35064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17532.5
Minimum1
Maximum35064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:09:11.518608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1754.15
Q18766.75
median17532.5
Q326298.25
95-th percentile33310.85
Maximum35064
Range35063
Interquartile range (IQR)17531.5

Descriptive statistics

Standard deviation10122.249
Coefficient of variation (CV)0.57734204
Kurtosis-1.2
Mean17532.5
Median Absolute Deviation (MAD)8766
Skewness0
Sum6.1475958 × 108
Variance1.0245993 × 108
MonotonicityStrictly increasing
2024-03-08T12:09:11.731095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
23379 1
 
< 0.1%
23373 1
 
< 0.1%
23374 1
 
< 0.1%
23375 1
 
< 0.1%
23376 1
 
< 0.1%
23377 1
 
< 0.1%
23378 1
 
< 0.1%
23380 1
 
< 0.1%
23422 1
 
< 0.1%
Other values (35054) 35054
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
35064 1
< 0.1%
35063 1
< 0.1%
35062 1
< 0.1%
35061 1
< 0.1%
35060 1
< 0.1%
35059 1
< 0.1%
35058 1
< 0.1%
35057 1
< 0.1%
35056 1
< 0.1%
35055 1
< 0.1%

year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
2016
8784 
2014
8760 
2015
8760 
2013
7344 
2017
1416 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters140256
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Length

2024-03-08T12:09:11.949115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:09:12.175209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Most occurring characters

ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5229295
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:09:12.363668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4487524
Coefficient of variation (CV)0.52871219
Kurtosis-1.2080577
Mean6.5229295
Median Absolute Deviation (MAD)3
Skewness-0.0092942217
Sum228720
Variance11.893893
MonotonicityNot monotonic
2024-03-08T12:09:12.520271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 2976
8.5%
5 2976
8.5%
7 2976
8.5%
8 2976
8.5%
10 2976
8.5%
12 2976
8.5%
1 2976
8.5%
4 2880
8.2%
6 2880
8.2%
9 2880
8.2%
Other values (2) 5592
15.9%
ValueCountFrequency (%)
1 2976
8.5%
2 2712
7.7%
3 2976
8.5%
4 2880
8.2%
5 2976
8.5%
6 2880
8.2%
7 2976
8.5%
8 2976
8.5%
9 2880
8.2%
10 2976
8.5%
ValueCountFrequency (%)
12 2976
8.5%
11 2880
8.2%
10 2976
8.5%
9 2880
8.2%
8 2976
8.5%
7 2976
8.5%
6 2880
8.2%
5 2976
8.5%
4 2880
8.2%
3 2976
8.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.729637
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:09:12.708818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8002175
Coefficient of variation (CV)0.55946729
Kurtosis-1.1940295
Mean15.729637
Median Absolute Deviation (MAD)8
Skewness0.0067598056
Sum551544
Variance77.443829
MonotonicityNot monotonic
2024-03-08T12:09:12.899784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1152
 
3.3%
2 1152
 
3.3%
28 1152
 
3.3%
27 1152
 
3.3%
26 1152
 
3.3%
25 1152
 
3.3%
24 1152
 
3.3%
23 1152
 
3.3%
22 1152
 
3.3%
21 1152
 
3.3%
Other values (21) 23544
67.1%
ValueCountFrequency (%)
1 1152
3.3%
2 1152
3.3%
3 1152
3.3%
4 1152
3.3%
5 1152
3.3%
6 1152
3.3%
7 1152
3.3%
8 1152
3.3%
9 1152
3.3%
10 1152
3.3%
ValueCountFrequency (%)
31 672
1.9%
30 1056
3.0%
29 1080
3.1%
28 1152
3.3%
27 1152
3.3%
26 1152
3.3%
25 1152
3.3%
24 1152
3.3%
23 1152
3.3%
22 1152
3.3%

hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros1461
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:09:13.136288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222853
Coefficient of variation (CV)0.60193785
Kurtosis-1.2041745
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum403236
Variance47.918033
MonotonicityNot monotonic
2024-03-08T12:09:13.357694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1461
 
4.2%
1 1461
 
4.2%
22 1461
 
4.2%
21 1461
 
4.2%
20 1461
 
4.2%
19 1461
 
4.2%
18 1461
 
4.2%
17 1461
 
4.2%
16 1461
 
4.2%
15 1461
 
4.2%
Other values (14) 20454
58.3%
ValueCountFrequency (%)
0 1461
4.2%
1 1461
4.2%
2 1461
4.2%
3 1461
4.2%
4 1461
4.2%
5 1461
4.2%
6 1461
4.2%
7 1461
4.2%
8 1461
4.2%
9 1461
4.2%
ValueCountFrequency (%)
23 1461
4.2%
22 1461
4.2%
21 1461
4.2%
20 1461
4.2%
19 1461
4.2%
18 1461
4.2%
17 1461
4.2%
16 1461
4.2%
15 1461
4.2%
14 1461
4.2%

PM2.5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct486
Distinct (%)1.4%
Missing779
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean65.989497
Minimum3
Maximum881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:09:13.569650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q114
median41
Q393
95-th percentile215
Maximum881
Range878
Interquartile range (IQR)79

Descriptive statistics

Standard deviation72.267723
Coefficient of variation (CV)1.0951398
Kurtosis5.5043159
Mean65.989497
Median Absolute Deviation (MAD)31
Skewness2.0077481
Sum2262449.9
Variance5222.6238
MonotonicityNot monotonic
2024-03-08T12:09:13.799014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1250
 
3.6%
7 797
 
2.3%
9 782
 
2.2%
6 781
 
2.2%
8 772
 
2.2%
10 762
 
2.2%
11 699
 
2.0%
5 683
 
1.9%
12 655
 
1.9%
4 608
 
1.7%
Other values (476) 26496
75.6%
(Missing) 779
 
2.2%
ValueCountFrequency (%)
3 1250
3.6%
4 608
1.7%
4.3 1
 
< 0.1%
5 683
1.9%
6 781
2.2%
7 797
2.3%
7.2 1
 
< 0.1%
7.9 1
 
< 0.1%
8 772
2.2%
8.4 1
 
< 0.1%
ValueCountFrequency (%)
881 1
< 0.1%
647 1
< 0.1%
632 1
< 0.1%
617 1
< 0.1%
614 1
< 0.1%
610 1
< 0.1%
604 1
< 0.1%
594 1
< 0.1%
576 1
< 0.1%
568 1
< 0.1%

PM10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct554
Distinct (%)1.6%
Missing656
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean83.739723
Minimum2
Maximum905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:09:14.060081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q126
median60
Q3117
95-th percentile239
Maximum905
Range903
Interquartile range (IQR)91

Descriptive statistics

Standard deviation79.541685
Coefficient of variation (CV)0.94986801
Kurtosis8.0714083
Mean83.739723
Median Absolute Deviation (MAD)40
Skewness2.1034336
Sum2881316.4
Variance6326.8796
MonotonicityNot monotonic
2024-03-08T12:09:14.297204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 628
 
1.8%
6 524
 
1.5%
13 429
 
1.2%
14 429
 
1.2%
10 420
 
1.2%
18 411
 
1.2%
11 404
 
1.2%
12 402
 
1.1%
16 397
 
1.1%
20 393
 
1.1%
Other values (544) 29971
85.5%
(Missing) 656
 
1.9%
ValueCountFrequency (%)
2 24
 
0.1%
3 186
 
0.5%
4 53
 
0.2%
5 628
1.8%
5.6 1
 
< 0.1%
6 524
1.5%
7 311
0.9%
7.9 2
 
< 0.1%
8 361
1.0%
8.4 1
 
< 0.1%
ValueCountFrequency (%)
905 1
< 0.1%
904 1
< 0.1%
895 1
< 0.1%
864 1
< 0.1%
842 1
< 0.1%
828 1
< 0.1%
811 1
< 0.1%
801 2
< 0.1%
792 1
< 0.1%
782 1
< 0.1%

SO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct327
Distinct (%)1.0%
Missing730
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean11.74965
Minimum0.2856
Maximum156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:09:14.481608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2856
5-th percentile2
Q12
median5
Q315
95-th percentile44
Maximum156
Range155.7144
Interquartile range (IQR)13

Descriptive statistics

Standard deviation15.519259
Coefficient of variation (CV)1.3208274
Kurtosis10.085321
Mean11.74965
Median Absolute Deviation (MAD)3
Skewness2.7703653
Sum403412.47
Variance240.8474
MonotonicityNot monotonic
2024-03-08T12:09:14.709693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 9638
27.5%
3 3766
 
10.7%
4 1833
 
5.2%
5 1425
 
4.1%
6 1280
 
3.7%
7 1049
 
3.0%
8 1043
 
3.0%
9 906
 
2.6%
10 864
 
2.5%
11 741
 
2.1%
Other values (317) 11789
33.6%
ValueCountFrequency (%)
0.2856 20
 
0.1%
0.5712 20
 
0.1%
0.8568 19
 
0.1%
1 473
 
1.3%
1.1424 15
 
< 0.1%
1.428 20
 
0.1%
1.7136 22
 
0.1%
1.9992 14
 
< 0.1%
2 9638
27.5%
2.2848 26
 
0.1%
ValueCountFrequency (%)
156 1
 
< 0.1%
150 1
 
< 0.1%
145 1
 
< 0.1%
141 1
 
< 0.1%
137 1
 
< 0.1%
136 2
< 0.1%
134 4
< 0.1%
131 1
 
< 0.1%
130 1
 
< 0.1%
129 2
< 0.1%

NO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct636
Distinct (%)1.9%
Missing1234
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean27.585467
Minimum1.0265
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:09:14.921268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.0265
5-th percentile2
Q19
median19
Q338
95-th percentile83
Maximum205
Range203.9735
Interquartile range (IQR)29

Descriptive statistics

Standard deviation26.383882
Coefficient of variation (CV)0.95644139
Kurtosis3.4365437
Mean27.585467
Median Absolute Deviation (MAD)12.2251
Skewness1.7030009
Sum933216.34
Variance696.10924
MonotonicityNot monotonic
2024-03-08T12:09:15.127928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 2841
 
8.1%
5 930
 
2.7%
8 915
 
2.6%
7 894
 
2.5%
10 891
 
2.5%
6 881
 
2.5%
4 871
 
2.5%
12 862
 
2.5%
9 859
 
2.4%
3 851
 
2.4%
Other values (626) 23035
65.7%
(Missing) 1234
 
3.5%
ValueCountFrequency (%)
1.0265 1
 
< 0.1%
1.2318 2
 
< 0.1%
1.4371 1
 
< 0.1%
2 2841
8.1%
2.4636 1
 
< 0.1%
2.8742 3
 
< 0.1%
3 851
 
2.4%
3.0795 2
 
< 0.1%
3.2848 1
 
< 0.1%
3.4901 2
 
< 0.1%
ValueCountFrequency (%)
205 1
 
< 0.1%
190.3131 1
 
< 0.1%
190 1
 
< 0.1%
188 1
 
< 0.1%
187.0283 1
 
< 0.1%
186 1
 
< 0.1%
184 3
< 0.1%
183 2
< 0.1%
182 2
< 0.1%
181.8958 1
 
< 0.1%

CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct103
Distinct (%)0.3%
Missing2012
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean904.89607
Minimum100
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:09:15.315840image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile200
Q1300
median600
Q31200
95-th percentile2700
Maximum10000
Range9900
Interquartile range (IQR)900

Descriptive statistics

Standard deviation903.30622
Coefficient of variation (CV)0.99824305
Kurtosis12.670412
Mean904.89607
Median Absolute Deviation (MAD)300
Skewness2.8056045
Sum29908625
Variance815962.13
MonotonicityNot monotonic
2024-03-08T12:09:15.521257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 4134
11.8%
300 3630
 
10.4%
400 3197
 
9.1%
500 2674
 
7.6%
600 2245
 
6.4%
700 1841
 
5.3%
800 1572
 
4.5%
900 1531
 
4.4%
100 1467
 
4.2%
1000 1255
 
3.6%
Other values (93) 9506
27.1%
(Missing) 2012
 
5.7%
ValueCountFrequency (%)
100 1467
 
4.2%
150 1
 
< 0.1%
200 4134
11.8%
300 3630
10.4%
350 1
 
< 0.1%
400 3197
9.1%
500 2674
7.6%
600 2245
6.4%
700 1841
5.3%
800 1572
 
4.5%
ValueCountFrequency (%)
10000 1
 
< 0.1%
9600 1
 
< 0.1%
9400 2
 
< 0.1%
9100 2
 
< 0.1%
9000 1
 
< 0.1%
8800 1
 
< 0.1%
8700 3
< 0.1%
8600 4
< 0.1%
8500 5
< 0.1%
8300 3
< 0.1%

O3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct792
Distinct (%)2.3%
Missing1214
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean68.548371
Minimum0.2142
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:09:15.731197image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2142
5-th percentile2
Q131
median61
Q390
95-th percentile175
Maximum500
Range499.7858
Interquartile range (IQR)59

Descriptive statistics

Standard deviation53.764424
Coefficient of variation (CV)0.78432825
Kurtosis3.7970949
Mean68.548371
Median Absolute Deviation (MAD)30
Skewness1.5273258
Sum2320362.4
Variance2890.6133
MonotonicityNot monotonic
2024-03-08T12:09:15.919313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1798
 
5.1%
69 344
 
1.0%
56 341
 
1.0%
54 336
 
1.0%
68 335
 
1.0%
72 329
 
0.9%
76 323
 
0.9%
75 323
 
0.9%
64 320
 
0.9%
65 319
 
0.9%
Other values (782) 29082
82.9%
(Missing) 1214
 
3.5%
ValueCountFrequency (%)
0.2142 6
 
< 0.1%
0.4284 6
 
< 0.1%
0.6426 5
 
< 0.1%
0.8568 5
 
< 0.1%
1 291
0.8%
1.071 6
 
< 0.1%
1.2852 4
 
< 0.1%
1.4994 2
 
< 0.1%
1.7136 3
 
< 0.1%
1.9278 5
 
< 0.1%
ValueCountFrequency (%)
500 5
< 0.1%
432 1
 
< 0.1%
411 1
 
< 0.1%
409 1
 
< 0.1%
403 1
 
< 0.1%
400 1
 
< 0.1%
389 1
 
< 0.1%
387 1
 
< 0.1%
385 1
 
< 0.1%
382 1
 
< 0.1%

TEMP
Real number (ℝ)

HIGH CORRELATION 

Distinct998
Distinct (%)2.9%
Missing53
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean13.686111
Minimum-16.6
Maximum41.4
Zeros186
Zeros (%)0.5%
Negative5115
Negative (%)14.6%
Memory size274.1 KiB
2024-03-08T12:09:16.392999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-16.6
5-th percentile-4
Q13.4
median14.7
Q323.3
95-th percentile30.7
Maximum41.4
Range58
Interquartile range (IQR)19.9

Descriptive statistics

Standard deviation11.365313
Coefficient of variation (CV)0.83042675
Kurtosis-1.134788
Mean13.686111
Median Absolute Deviation (MAD)9.7
Skewness-0.098545321
Sum479164.44
Variance129.17034
MonotonicityNot monotonic
2024-03-08T12:09:16.593679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 240
 
0.7%
1 224
 
0.6%
-2 205
 
0.6%
2 202
 
0.6%
0 186
 
0.5%
-1 169
 
0.5%
4 149
 
0.4%
21.5 145
 
0.4%
22.9 142
 
0.4%
24.3 137
 
0.4%
Other values (988) 33212
94.7%
ValueCountFrequency (%)
-16.6 1
 
< 0.1%
-16.5 1
 
< 0.1%
-16.2 1
 
< 0.1%
-16.1 1
 
< 0.1%
-15.9 1
 
< 0.1%
-15.8 2
< 0.1%
-15.6 1
 
< 0.1%
-15.5 2
< 0.1%
-15.4 2
< 0.1%
-15.3 3
< 0.1%
ValueCountFrequency (%)
41.4 1
< 0.1%
41 1
< 0.1%
40.5 2
< 0.1%
40 1
< 0.1%
39.8 2
< 0.1%
39.2 1
< 0.1%
38.9 1
< 0.1%
38.5 1
< 0.1%
38.4 1
< 0.1%
38.3 1
< 0.1%

PRES
Real number (ℝ)

HIGH CORRELATION 

Distinct593
Distinct (%)1.7%
Missing50
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1007.7603
Minimum982.4
Maximum1036.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:09:16.820489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum982.4
5-th percentile992.4
Q1999.3
median1007.4
Q31016
95-th percentile1024.3
Maximum1036.5
Range54.1
Interquartile range (IQR)16.7

Descriptive statistics

Standard deviation10.225664
Coefficient of variation (CV)0.010146921
Kurtosis-0.91808003
Mean1007.7603
Median Absolute Deviation (MAD)8.4
Skewness0.10384267
Sum35285718
Variance104.56419
MonotonicityNot monotonic
2024-03-08T12:09:17.035086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1019 295
 
0.8%
1021 286
 
0.8%
1018 266
 
0.8%
1015 246
 
0.7%
1022 242
 
0.7%
1020 241
 
0.7%
1017 235
 
0.7%
1016 234
 
0.7%
1014 213
 
0.6%
1011 198
 
0.6%
Other values (583) 32558
92.9%
ValueCountFrequency (%)
982.4 1
< 0.1%
982.7 1
< 0.1%
982.8 1
< 0.1%
982.9 1
< 0.1%
983 1
< 0.1%
983.2 2
< 0.1%
983.3 1
< 0.1%
983.5 2
< 0.1%
983.7 2
< 0.1%
984 2
< 0.1%
ValueCountFrequency (%)
1036.5 1
 
< 0.1%
1036.3 1
 
< 0.1%
1036.2 1
 
< 0.1%
1036.1 1
 
< 0.1%
1036 3
< 0.1%
1035.9 3
< 0.1%
1035.8 1
 
< 0.1%
1035.7 1
 
< 0.1%
1035.6 1
 
< 0.1%
1035.5 1
 
< 0.1%

DEWP
Real number (ℝ)

HIGH CORRELATION 

Distinct602
Distinct (%)1.7%
Missing53
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1.5054954
Minimum-35.1
Maximum27.2
Zeros66
Zeros (%)0.2%
Negative16287
Negative (%)46.4%
Memory size274.1 KiB
2024-03-08T12:09:17.244615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-35.1
5-th percentile-20.5
Q1-10.2
median1.8
Q314.2
95-th percentile21.4
Maximum27.2
Range62.3
Interquartile range (IQR)24.4

Descriptive statistics

Standard deviation13.822099
Coefficient of variation (CV)9.1810966
Kurtosis-1.176748
Mean1.5054954
Median Absolute Deviation (MAD)12.3
Skewness-0.14823158
Sum52708.9
Variance191.05042
MonotonicityNot monotonic
2024-03-08T12:09:17.444780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.6 137
 
0.4%
15 133
 
0.4%
15.7 126
 
0.4%
16.1 122
 
0.3%
15.3 120
 
0.3%
16.8 120
 
0.3%
16.7 119
 
0.3%
15.8 118
 
0.3%
17.5 116
 
0.3%
16.2 116
 
0.3%
Other values (592) 33784
96.3%
ValueCountFrequency (%)
-35.1 1
< 0.1%
-34.4 2
< 0.1%
-34.2 1
< 0.1%
-33.8 2
< 0.1%
-33.7 1
< 0.1%
-33.5 1
< 0.1%
-33.4 1
< 0.1%
-33 2
< 0.1%
-32.8 1
< 0.1%
-32.5 1
< 0.1%
ValueCountFrequency (%)
27.2 2
 
< 0.1%
27.1 2
 
< 0.1%
27 2
 
< 0.1%
26.9 5
< 0.1%
26.8 4
 
< 0.1%
26.7 2
 
< 0.1%
26.6 3
 
< 0.1%
26.5 1
 
< 0.1%
26.4 5
< 0.1%
26.3 10
< 0.1%

RAIN
Real number (ℝ)

SKEWED  ZEROS 

Distinct116
Distinct (%)0.3%
Missing51
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.06036615
Minimum0
Maximum52.1
Zeros33663
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:09:17.661036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum52.1
Range52.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75289931
Coefficient of variation (CV)12.47221
Kurtosis1270.9726
Mean0.06036615
Median Absolute Deviation (MAD)0
Skewness29.569063
Sum2113.6
Variance0.56685737
MonotonicityNot monotonic
2024-03-08T12:09:17.900200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33663
96.0%
0.1 287
 
0.8%
0.2 146
 
0.4%
0.3 122
 
0.3%
0.5 79
 
0.2%
0.4 71
 
0.2%
0.7 52
 
0.1%
0.6 44
 
0.1%
0.9 42
 
0.1%
1 42
 
0.1%
Other values (106) 465
 
1.3%
(Missing) 51
 
0.1%
ValueCountFrequency (%)
0 33663
96.0%
0.1 287
 
0.8%
0.2 146
 
0.4%
0.3 122
 
0.3%
0.4 71
 
0.2%
0.5 79
 
0.2%
0.6 44
 
0.1%
0.7 52
 
0.1%
0.8 42
 
0.1%
0.9 42
 
0.1%
ValueCountFrequency (%)
52.1 1
< 0.1%
37.4 1
< 0.1%
28.9 1
< 0.1%
28.7 1
< 0.1%
26.5 1
< 0.1%
25.3 1
< 0.1%
23.7 1
< 0.1%
22.7 1
< 0.1%
22.3 1
< 0.1%
21.6 1
< 0.1%

wd
Categorical

Distinct16
Distinct (%)< 0.1%
Missing140
Missing (%)0.4%
Memory size274.1 KiB
NNW
4776 
NW
3838 
N
3777 
WNW
2877 
ESE
2786 
Other values (11)
16870 

Length

Max length3
Median length2
Mean length2.2299278
Min length1

Characters and Unicode

Total characters77878
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowENE
3rd rowENE
4th rowNNE
5th rowN

Common Values

ValueCountFrequency (%)
NNW 4776
13.6%
NW 3838
10.9%
N 3777
10.8%
WNW 2877
8.2%
ESE 2786
 
7.9%
E 2427
 
6.9%
NNE 1919
 
5.5%
SSE 1853
 
5.3%
SE 1823
 
5.2%
NE 1721
 
4.9%
Other values (6) 7127
20.3%

Length

2024-03-08T12:09:18.124812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nnw 4776
13.7%
nw 3838
11.0%
n 3777
10.8%
wnw 2877
8.2%
ese 2786
 
8.0%
e 2427
 
6.9%
nne 1919
 
5.5%
sse 1853
 
5.3%
se 1823
 
5.2%
ne 1721
 
4.9%
Other values (6) 7127
20.4%

Most occurring characters

ValueCountFrequency (%)
N 26908
34.6%
W 19194
24.6%
E 17925
23.0%
S 13851
17.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 77878
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 26908
34.6%
W 19194
24.6%
E 17925
23.0%
S 13851
17.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 77878
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 26908
34.6%
W 19194
24.6%
E 17925
23.0%
S 13851
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77878
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 26908
34.6%
W 19194
24.6%
E 17925
23.0%
S 13851
17.8%

WSPM
Real number (ℝ)

ZEROS 

Distinct95
Distinct (%)0.3%
Missing43
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.8538363
Minimum0
Maximum10
Zeros417
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:09:18.360664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q11
median1.5
Q32.3
95-th percentile4.7
Maximum10
Range10
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.3098083
Coefficient of variation (CV)0.70653938
Kurtosis3.2071389
Mean1.8538363
Median Absolute Deviation (MAD)0.6
Skewness1.6592831
Sum64923.2
Variance1.7155979
MonotonicityNot monotonic
2024-03-08T12:09:18.576623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 1934
 
5.5%
1.1 1928
 
5.5%
1 1922
 
5.5%
1.3 1859
 
5.3%
0.9 1787
 
5.1%
1.4 1589
 
4.5%
0.8 1569
 
4.5%
1.5 1524
 
4.3%
1.6 1443
 
4.1%
0.7 1351
 
3.9%
Other values (85) 18115
51.7%
ValueCountFrequency (%)
0 417
 
1.2%
0.1 201
 
0.6%
0.2 215
 
0.6%
0.3 157
 
0.4%
0.4 474
 
1.4%
0.5 722
2.1%
0.6 1043
3.0%
0.7 1351
3.9%
0.8 1569
4.5%
0.9 1787
5.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9.6 3
< 0.1%
9.4 1
 
< 0.1%
9.3 3
< 0.1%
9.2 3
< 0.1%
9.1 1
 
< 0.1%
9 1
 
< 0.1%
8.8 3
< 0.1%
8.6 2
 
< 0.1%
8.5 7
< 0.1%

station
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
Dingling
35064 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters280512
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDingling
2nd rowDingling
3rd rowDingling
4th rowDingling
5th rowDingling

Common Values

ValueCountFrequency (%)
Dingling 35064
100.0%

Length

2024-03-08T12:09:18.791166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:09:18.945185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
dingling 35064
100.0%

Most occurring characters

ValueCountFrequency (%)
i 70128
25.0%
n 70128
25.0%
g 70128
25.0%
D 35064
12.5%
l 35064
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 245448
87.5%
Uppercase Letter 35064
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 70128
28.6%
n 70128
28.6%
g 70128
28.6%
l 35064
14.3%
Uppercase Letter
ValueCountFrequency (%)
D 35064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 280512
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 70128
25.0%
n 70128
25.0%
g 70128
25.0%
D 35064
12.5%
l 35064
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 280512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 70128
25.0%
n 70128
25.0%
g 70128
25.0%
D 35064
12.5%
l 35064
12.5%

Interactions

2024-03-08T12:09:07.356211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:32.570920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:35.110313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:37.857627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:40.539883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:42.943102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:45.728946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:48.193970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:50.395787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:52.883783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:55.425430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:58.047634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:00.253346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:02.685164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:05.130990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:07.549387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:32.823387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:35.566148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:38.129666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:40.693584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:43.179354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:45.877818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:48.347018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:50.542973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:53.021572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:55.613006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:58.239609image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:00.404056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:02.853471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:05.300598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:07.697403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:32.979022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:35.696696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:38.289993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:40.846986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:43.305196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:46.032977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:48.488817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:50.745962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:53.423381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:55.828940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:58.394110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:00.572965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:02.994105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:05.417755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:07.847458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:33.153209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:35.835308image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:38.455186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:40.999893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:43.459727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:46.173900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:48.645632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:50.882889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:53.644739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:56.064337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:58.522839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:00.693785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:03.133128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:05.607647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:07.986194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:33.285109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:35.958867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:38.653261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:41.144414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:43.608801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:46.309791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:48.803758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:51.038223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:53.779948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:56.328285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:58.674701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:00.818401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:03.253104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:05.731105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:08.137427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:33.419971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:36.113346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:38.817859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:41.293225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:43.766428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:46.482023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:48.951175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:51.219739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:53.929816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:56.489428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:58.809524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:00.956282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:03.384660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:05.876245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:08.339084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:33.598620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:36.283775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:39.007793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:41.436725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:43.903312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:46.628695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:49.093775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:51.371244image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:54.051256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:56.633253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:58.935284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:01.096256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:03.523265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:06.028677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:08.499948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:33.767303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:36.477180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:39.235823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:41.644771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:44.057374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:46.777924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:49.256708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:51.548772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:54.220589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:56.793965image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:59.097925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:01.289623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:03.678850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:06.181673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:08.678164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:33.930704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:36.647216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:39.391220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:41.829864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:44.271899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:46.921621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:49.405797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:51.718188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:54.390001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:56.966864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:59.249753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:01.446908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:03.837789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:06.346047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:08.801579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:34.108583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:36.820813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:39.597157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:41.981874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:44.477471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:47.054229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:49.560234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:51.877198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:54.538023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:57.113952image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:59.372091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:01.599007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:03.982238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:06.489864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:08.952089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:34.260743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:36.991365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:39.766543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:42.113610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:44.633656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:47.244772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:49.714851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:52.040589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:54.668549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:57.250306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:59.512091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:01.748396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:04.118228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:06.630920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:09.110677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:34.417819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:37.171476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:39.922689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:42.266028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:45.183569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:47.430484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:49.837940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:52.243562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:54.820272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:57.426187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:59.642981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:01.881758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:04.317412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:06.751942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:09.281098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:34.553854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:37.357154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:40.066332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:42.407008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:45.296214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:47.637868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:49.971439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:52.405360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:54.986393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:57.570861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:59.794406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:02.031393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:04.484371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:06.882332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:09.439556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:34.721518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:37.531569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:40.219447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:42.581297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:45.442111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:47.847450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:50.119007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:52.578682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:55.162121image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:57.766555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:59.939839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:02.176549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:04.715508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:07.020696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:09.642020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:34.898447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:37.673145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:40.385374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:42.753977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:45.578644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:48.069080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:50.263058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:52.737174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:55.281265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:57.909152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:00.101917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:02.528548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:04.906604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:09:07.167607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-03-08T12:09:19.079257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CODEWPNO2NoO3PM10PM2.5PRESRAINSO2TEMPWSPMdayhourmonthwdyear
CO1.0000.1290.792-0.002-0.3890.7190.8350.0970.0420.565-0.167-0.3420.0030.0150.0520.0620.067
DEWP0.1291.000-0.001-0.0820.2570.1260.249-0.7700.182-0.3470.820-0.2370.0250.0000.2580.1080.150
NO20.792-0.0011.000-0.064-0.4680.7020.7600.132-0.0170.610-0.225-0.3380.0100.0350.0430.0960.057
No-0.002-0.082-0.0641.000-0.054-0.033-0.0540.1640.019-0.178-0.1300.0810.0180.0010.0440.1000.862
O3-0.3890.257-0.468-0.0541.000-0.118-0.209-0.463-0.035-0.2330.5890.398-0.0090.242-0.2070.1350.063
PM100.7190.1260.702-0.033-0.1181.0000.887-0.076-0.0650.470-0.002-0.1840.0260.091-0.0720.0850.046
PM2.50.8350.2490.760-0.054-0.2090.8871.000-0.087-0.0010.4730.001-0.3100.0200.035-0.0180.0770.052
PRES0.097-0.7700.1320.164-0.463-0.076-0.0871.000-0.0770.309-0.8410.0380.019-0.0400.0110.0730.144
RAIN0.0420.182-0.0170.019-0.035-0.065-0.001-0.0771.000-0.1170.032-0.051-0.0040.0070.0530.0140.006
SO20.565-0.3470.610-0.178-0.2330.4700.4730.309-0.1171.000-0.356-0.041-0.0120.060-0.1450.0530.093
TEMP-0.1670.820-0.225-0.1300.589-0.0020.001-0.8410.032-0.3561.0000.0830.0150.1430.1260.1200.150
WSPM-0.342-0.237-0.3380.0810.398-0.184-0.3100.038-0.051-0.0410.0831.000-0.0150.139-0.1300.1800.077
day0.0030.0250.0100.018-0.0090.0260.0200.019-0.004-0.0120.015-0.0151.0000.0000.0100.0260.000
hour0.0150.0000.0350.0010.2420.0910.035-0.0400.0070.0600.1430.1390.0001.0000.0000.1670.000
month0.0520.2580.0430.044-0.207-0.072-0.0180.0110.053-0.1450.126-0.1300.0100.0001.0000.0850.249
wd0.0620.1080.0960.1000.1350.0850.0770.0730.0140.0530.1200.1800.0260.1670.0851.0000.103
year0.0670.1500.0570.8620.0630.0460.0520.1440.0060.0930.1500.0770.0000.0000.2490.1031.000

Missing values

2024-03-08T12:09:09.967385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-08T12:09:10.421620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-08T12:09:11.126824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
0120133104.04.03.0NaN200.082.0-2.31020.8-19.70.0E0.5Dingling
1220133117.07.03.0NaN200.080.0-2.51021.3-19.00.0ENE0.7Dingling
2320133125.05.03.02.0200.079.0-3.01021.3-19.90.0ENE0.2Dingling
3420133136.06.03.0NaN200.079.0-3.61021.8-19.10.0NNE1.0Dingling
4520133145.05.03.0NaN200.081.0-3.51022.3-19.40.0N2.1Dingling
5620133156.06.03.04.0200.079.0-4.51022.6-19.50.0NNW1.7Dingling
6720133165.010.03.04.0200.077.0-4.51023.4-19.50.0NNW1.8Dingling
7820133175.06.03.02.0200.080.0-2.11024.6-20.00.0NW2.5Dingling
8920133188.07.03.03.0200.079.0-0.21025.2-20.50.0NNW2.8Dingling
91020133198.08.03.02.0200.081.00.61025.3-20.40.0NNW3.8Dingling
NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
350543505520172281410.026.04.06.0300.092.014.91008.4-12.40.0WNW4.2Dingling
350553505620172281516.027.06.0NaN400.088.015.61007.6-12.80.0WNW3.2Dingling
350563505720172281614.030.0NaN6.0NaN93.015.41007.2-12.90.0WNW4.4Dingling
350573505820172281715.039.04.08.0200.094.014.71007.4-12.60.0WNW4.2Dingling
35058350592017228186.025.02.02.0200.099.013.41008.1-13.60.0WNW3.0Dingling
350593506020172281911.011.02.02.0200.099.011.71008.9-13.30.0NNE1.3Dingling
350603506120172282013.013.02.02.0200.0101.010.91009.0-14.00.0N2.1Dingling
35061350622017228219.014.02.02.0200.0102.09.51009.4-13.00.0N1.5Dingling
350623506320172282210.012.02.02.0200.097.07.81009.6-12.60.0NW1.4Dingling
350633506420172282313.016.04.09.0500.074.07.01009.4-12.20.0N1.9Dingling